Design Principles and Mechanistic Understandings of Non-Noble-Metal Bifunctional Electrocatalysts for Zinc-Air Batteries

Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs.Therefore,feasible and advanced non-noble-metal elec-trocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction.In this review,we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field.Then,we discussed the work-ing mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design,crystal structure tuning,interface strategy,and atomic engineering.We also included theoretical studies,machine learning,and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions.Finally,we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.

The Four Dimensional Connotation Definition of Scientific Understanding of the Ocean and Human Practical Achievements

By analyzing the sustainable development process of strengthening marine environmental protection and global reporting and assessment of marine environmental conditions since the Human Environment Conference, this paper summarizes the scientific connotation of “scientific understanding of the ocean” reflected in the United Nations Global Marine Environmental Assessment Report, proposes the etymological definition and specific coverage, representative global and regional practical experience of scientific understanding of the ocean, and further analyzes and defines the human activities and cognitive evolution process of “scientific understanding of the ocean”. It marks the leap in human cognition in four dimensions: observation and evaluation, intervention and regulation, disciplinary knowledge system, and supporting guarantee system. It condenses the connotation definitions and human practical achievements of each dimension, and puts forward countermeasures and suggestions to strengthen marine environmental protection and sustainable development.

What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of “what if this has happened differently”. Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and present counterfactual samples cannot help users gain a deeper understanding of the system. Without a way to verify their understanding, the users can even be misled by such explanations. Such limitations can be overcome through an interactive and iterative framework that allows the users to explore their desired “what-if” scenarios. The purpose of our research is to develop such a framework. In this paper, we present our “what-if” XAI framework (WiXAI), which visualizes the artificial intelligence (AI) classification model from the perspective of the user’s sample and guides their “what-if” exploration. We also formulated how to use the WiXAI framework to generate counterfactuals and understand the feature-feature and feature-output relations in-depth for a local sample. These relations help move the users toward causal understanding.

A Study on High School Mathematics Teaching Design Based on Teaching for Robust Understanding: Taking the Cosine Theorem as an Example

Teachers’teaching behavior plays a crucial role in students’development,and there are problems in the current teaching behavior of mathematics teachers such as ignoring students’cognitive needs,lack of equal opportunities for students’classroom performance as well as lack of formative evaluation of students.In order to solve the phenomenon,this paper analyzes and explains how to promote teaching based on the Teaching for Robust Understanding(TRU)evaluation framework with the goal of focusing on the development of all students,taking the teaching design of The Cosine Theorem as an example,and provides ideas and methods for first-line high school mathematics teachers.

Electrochemical Carbon Dioxide Reduction to Ethylene:From Mechanistic Understanding to Catalyst Surface Engineering

Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to chemicals.The multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial applications.However,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products.Nonetheless,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)RR.In this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to ethylene.Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene generation.Engineering strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are elaborated.Finally,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.

The Effect of Latinization on Reading Time and Understanding: Greeklish in Communication and Social Media

The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known as Latinization, also employed for many non-latin alphabet languages. The primary aim of this research is to evaluate the effect of Greeklish on reading time. A sample of 732 young Greeks were asked about their habits when communicating through e-mail and social media with their friends and they then participated in an experiment in which they were asked to read and understand two short texts, one written in Greek and the other in Greeklish. The findings of the research show that nearly one third of the participants use Greeklish. The results of the experiment conducted reveal that understanding is not affected by the alphabet used but reading Greeklish is significantly more time consuming than reading Greek independently of the sex and the familiarity of the participants with Greeklish. The findings suggest that amending social and communication media with software utilities related to Latinization such as language identifiers and converters may reduce reading time and thus facilitate written communication among the users.

Solving Algebraic Problems with Geometry Diagrams Using Syntax-Semantics Diagram Understanding

Solving Algebraic Problems with Geometry Diagrams(APGDs)poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects.Problems typically involve both textual descriptions and geometry diagrams,requiring a joint understanding of these modalities.Although considerable progress has been made in solving math word problems,research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs,which limits their ability to effectively solve problems.In this study,a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and diagrammatic information.The three-phase scheme begins with the application of the statetransformer paradigm,modeling the problem-solving process and effectively representing the intermediate states and transformations during the process.Next,a generalized APGD-solving approach is introduced to effectively extract geometric knowledge from the problem’s textual descriptions and diagrams.Finally,a specific algorithm is designed focusing on diagram understanding,which utilizes the vectorized syntax-semantics model to extract basic geometric relations from the diagram.A method for generating derived relations,which are essential for solving APGDs,is also introduced.Experiments on real-world datasets,including geometry calculation problems and shaded area problems,demonstrate that the proposed diagram understanding method significantly improves problem-solving accuracy compared to methods relying solely on simple diagram parsing.

The Chinese Association for International Understanding Holds Lecture II of the Civilisation Lecture Series

On December 6th,2023,the Chinese Association for International Understanding(CAFIU)held Lecture II of the Civilisation Lecture Series in Paramaribo,the capital of Suriname.Ai Ping,Vice-President of CAFIU,delivered a keynote speech.Han Jing,the Chinese Ambassador to Suriname,delivered a speech.

Tiêu Equation Experimentation of Understanding by Energy Transfer Quantum Mechanics

Background: The Tiêu equation has a ground roots approach to the process of Quantum Biology and goes deeper through the incorporation of Quantum Mechanics. The process can be measured in plant, animal, and human usage through a variety of experimental or testing forms. Animal studies were conducted for which, in the first day of the study all the animals consistently gained dramatic weight, even as a toxic substance was introduced as described in the introduction of the paper to harm animal subjects which induced weight loss through toxicity. Tests can be made by incorporating blood report results. Human patients were also observed to show improvement to their health as administration of the substance was introduced to the biological mechanism and plants were initially exposed to the substance to observe results. This is consistent with the Tiêu equation which provides that wave function is created as the introduction of the substance to the biological mechanism which supports Quantum Mechanics. The Tiêu equation demonstrates that Quantum Mechanics moves a particle by temperature producing energy thru the blood-brain barrier for example. Methods: The methods for the Tiêu equation incorporate animal studies to include the substance administered through laboratory standards using Good Laboratory Practices under Title 40 C.F.R. § 158. Human patients were treated with the substance by medical professionals who are experts in their field and have knowledge to the response of patients. Plant applications were acquired for observation and guidance of ongoing experiments of animals’ representative for the biologics mechanism. Results: The animal studies along with patient blood testing results have been an impressive line that has followed the Tiêu equation to consistently show improvement in the introduction of the innovation to biologic mechanisms. The mechanism responds to the substance by producing energy to the mechanism with efficient effect. For plant observations, plant organisms responded, and were seen as showing improvement thru visual observation.

Current Situation and Implementation Paths of International Understanding Education in Local Application-oriented Universities

It is urgent and necessary to implement international understanding education in local application-oriented colleges and universities.Improving the international competitiveness of talents and cultivating global citizens is one of the goals of higher education.This paper discussed the problems of international understanding education in local application-oriented colleges and universities,including weak policy orientation,insufficient practical exploration,aphasia of national culture,etc.It is recommended to implement the international understanding education from two ways:subject penetration and project-based professional courses.In addition,the introduction of Chinese culture cannot be ignored to prevent the absence of national culture in cross-cultural communication.

中国石化“十四五”主要勘探进展与发展战略

在系统梳理中国石油化工股份有限公司(简称中国石化)“十四五”以来高质量勘探举措、理论技术进展及油气勘探成果的基础上,提出了油气勘探发展战略。“十四五”以来,中国石化坚定资源发展信心,大力推动高质量勘探实践,在塔里木、四川、渤海湾、准噶尔、鄂尔多斯等盆地的深层碳酸盐岩、陆相页岩油、深层页岩气、中西部碎屑岩、成熟探区隐蔽油气藏、深层煤层气等领域取得了主要勘探成果,为石油稳增长、天然气快上产夯实了储量基础。同时,发展完善了超深走滑断控缝洞型油气成藏理论、创新形成了页岩油成储成烃富集理论、深化了海相页岩气“二元富集”与致密碎屑岩油气差异富集机理认识等;攻关形成了复杂地质结构与储层结构条件下的以地震勘探、深层—超深层钻完井、超深层测录试井、水平井压裂技术等为代表的关键技术与工程工艺技术。展望未来,中国石化探区具备石油稳定发展、天然气快速发展的资源基础,勘探工作聚焦保拓矿权、勘探突破、规模增储三大核心责任,以深地工程、页岩油气示范工程、致密碎屑岩效益攻坚、常非统筹为统领,加快推进战略突破发现,落实规模增储阵地,进一步夯实上游可持续发展资源基础。

联合多意图识别与语义槽填充的双向交互模型

意图识别与语义槽填充是口语理解的两个主要任务,两者具有高度相关性,通常进行联合训练。随着口语理解任务的深入,研究发现用户在现实场景中的话语往往含有多个意图。但部分联合模型只能识别用户话语中的单个意图,未能充分建模多个意图和语义槽之间的关联性。考虑到话语中多个意图的信息可以引导语义槽填充,语义槽信息也可以帮助意图更好的识别,模型采用图注意力网络建立意图和语义槽之间的双向交互。具体的,将两个任务双向关联以便模型能够挖掘多个意图与语义槽之间的关系,同时引入两个任务的标签信息使模型能够学习到话语上下文和标签的关系,从而提高意图识别与语义槽填充的准确率,优化口语理解的整体性能。实验表明,模型在MixATIS和MixSNIPS两个多意图数据集上对比其他模型性能得到了显著提升。

“红色经典”海外传播与国际社会对中国的认知

作为与当代社会发展结合紧密的文学艺术类型,“红色经典”的海外传播同国际社会对中国的认知有着内在的关联。“红色经典”在20世纪50—70年代集中传播到欧美和亚洲的一些国家,建构起关于“红色经典”的知识再生产;而国外知识界特别是美国和法国左翼知识分子对红色中国的认同与赞赏起到了消解彼时西方主流社会对中国负面宣传的作用,他们对毛泽东思想的热衷与“红色经典”所反映的中国革命和社会主义建设成就形成互文、互动及互鉴。新世纪以来,国外文学文化领域对我国“红色经典”改编的关注,同近年来国外经济政治领域渴望了解并热衷探讨当代中国成功发展经验的内在需求,形成某种隐在的呼应。

中外文化交流与传播背景下高职英语教学中文化理解与传承素养的培育研究

从高职英语教学中培养学生文化理解与传承素养的重要性出发,结合教学实践调查高职英语教学中学生文化理解与传承素养培育存在的问题,从高职英语的课堂教学、课外活动以及英语教师文化素养提升三个方面探索高职学生文化理解与传承素养培育的策略与实现路径,旨在培养学生中华文化理解与传承素养,使其在推动中外文化交流与传播的过程中发挥其有效作用。

矿物组分识别与智能解释在不同岩性之间的信息共享与迁移学习

在地球科学领域,岩石微观观测数据的采集过程繁琐且效率低下,这不仅增加了研究成本,降低了可靠性,同时也限制了数据的开源共享。此外,由于岩性的多样性和观测手段的差异,单一数据集的规模通常较小,这对于依赖大规模数据集的深度学习框架而言是一大挑战。为此,本研究探索迁移学习如何促进不同岩性间的信息共享,并通过此机制提高矿物组分识别与智能解释任务的模型性能。通过采集不同区域、岩性、矿物组分和偏光模式下的铸体薄片样本,本文深入研究了深度学习模型在不同观测对象和手段下的迁移学习机制,并聚焦于探索地质信息的深层表征。研究成果不但揭示了迁移学习在促进地质学领域信息共享与模型性能提升中的关键作用,还为自动化和智能化地质认识融合奠定了基础。实验结果显示,通过迁移学习,本文模型在智能解释任务中的准确率显著提高,从53.3%提高至98.73%,而在矿物组分识别任务中,准确率也实现了近10%的提升。这些成果证明了迁移学习在地质学领域内解决实际问题和提高模型泛化能力、性能和稳定性方面的巨大潜力。

融合知识图谱的影视视频标签分类算法研究

鉴于视频感知方式的多样性,视频标签层级分类算法均从视觉和文本模态入手,训练联合模型共同推断视频内容。但现有研究通常只适用于粗粒度的分类,针对影视剧名的分类,则需要更加细粒度的识别。提出了一个融合知识图谱的影视视频标签分类算法。首先,使用了基于大规模通用数据训练的多模态预训练模型提取了视觉和文本的特征,训练了一个多任务的视频标签预测模型,得到视频的类型、题材和实体三级标签;通过在多任务学习网络中引入相似性任务提高分类模型训练的难度,使得同类样本特征更加紧密,且更好地表达样本差异。其次,对于最细粒度的实体标签,提出了一个局部注意力头扩展的实体纠错模型,引入外部知识图谱的共现信息对前置模型的预测结果做修正,得到更准确的实体标签预测结果。采集豆瓣的半结构化数据构建了影视知识图谱并对影视视频标签分类模型进行了实证研究。视频标签分类的实验结果表明,首先,基于多任务网络结构,在训练分类任务时加入交叉熵损失函数和相似性损失函数对模型进行共同约束优化了特征表达,在类型、题材、实体标签的Top-1分类准确率上分别提升了3.70%、3.35%和16.57%;其次,针对前置模型的困难样本提出的全局-局部注意力机制模型,在引入了知识图谱信息之后,实体标签的Top-1分类准确率从38.7%提升到45.6%。该研究是使用图片-文本对数据在多模态视频标签分类问题上新的尝试,为少量数据样本情况下的短视频标签分类提供了新的研究思路。

基于微观连续介质模型的反应溶质运移数值模拟方法

地下环境中流固间的反应溶质运移过程控制着地质介质演化规律,并受岩石固有非均质性的影响,衍生出非线性行为与多尺度效应。为了面向更为复杂化的应用需求,解析微观特征与表观行为间的响应-反馈机制成为了重要的科学与工程命题。本研究系统地介绍了微观连续介质概念与模型框架,以及该模型方法的实现方式,并归纳了其在不同研究体系中的应用实例,最后重点分析了该方法在实验-数值联合研究中的发展前景与预期挑战。数值模拟方法已被广泛应用于反应溶质运移过程的定量研究之中,但单一孔隙或达西尺度数值模型难以应对机理研究与实例应用间的时空尺度跨度。近年来多尺度数值模拟方法体系不断发展,其中将Darcy-Brinkman-Stokes方程作为多流态数学统一表达,以微观连续介质模型作为多尺度信息关联方式的数值模拟框架,在流固界面追踪、高效数值求解等方面展现出一定的方法优势与应用潜力。

基于动作条件交互的高效行人过街意图预测

城市化的进程不断加速,人车冲突问题已成为现代社会亟待解决的重大难题。复杂交通场景下,行人横穿马路行为导致交通事故频发,准确、实时地预测行人过街意图对避免人车冲突、提高驾驶安全系数和保障行人安全至关重要。本文提出基于动作条件交互的高效行人过街意图预测框架(efficient action-conditioned interaction pedestrian crossing intention anticipation framework,EAIPF)来预测行人过街意图。EAIPF引入行人动作编码模块增强多模态动作模式下的表征能力,挖掘深层骨架上下文信息。同时,引入场景对象交互模块挖掘与对象交互信息,理解交通场景中高级语义线索。最后,意图预测模块融合行人动作特征和对象交互特征,实现行人过街意图的鲁棒预测。所提出的方法在两个公共数据集JAAD和PIE上验证算法性能,准确率分别达到了89%和90%,表明本文方法可以在复杂交通场景下准确预测行人穿越意图。

基于场景理解的施工临边坠落险兆智能识别方法

为更及时、更有效地预防施工临边坠落事故的发生,并弥补现有智能预警方法在场景理解方面的不足,融合深度学习与语义推理,提出1种险兆识别方法。该方法通过neo4j构建险兆知识图谱,将引入轻量级视觉Transformer的YOLOx模型识别工人的险兆行为,设计描述空间关系的IoU计算方法并使用Cypher推理语言进行险兆推理。研究结果表明:施工临边坠落各要素识别的平均精度达91%以上,且IoU计算与险兆推理准确率均为100%,模型识别效果与险兆推理效果较好,该方法总体满足精度和速度的识别要求。研究结果可为实现施工临边坠落险兆行为的精准识别和预警提供参考。

基于全局与序列混合变分Transformer的多样化图像描述生成方法

多样化图像描述生成已成为图像描述领域研究热点.然而,现有方法忽视了全局和序列隐向量之间的依赖关系,严重限制了图像描述性能的提升.针对该问题,本文提出了基于混合变分Transformer的多样化图像描述生成框架.具体地,首先构建全局与序列混合条件变分自编码模型,解决全局与序列隐向量之间依赖关系表示的问题.其次,通过最大化条件似然推导混合模型的变分证据下界,解决多样化图像描述目标函数设计问题.最后,无缝融合Transformer和混合变分自编码模型,通过联合优化提升多样化图像描述的泛化性能.在MSCOCO数据集上实验结果表明,与当前最优基准方法相比,在随机生成20和100个描述语句时,多样性指标m-BLEU(mutual overlap-BiLingual Evaluation Understudy)分别提升了4.2%和4.7%,同时准确性指标CIDEr(Consensus-based Image Description Evaluation)分别提升了4.4%和15.2%.